1. Field of the Invention
The present invention relates to the demodulation of digitally modulated signals and more particularly, to such signals received through radio channels subjected to the interference phenomenon such as fading, distortion and intersymbol interference.
2. History of the Related Art
In the communication of digitally modulated radio signals such as are employed in mobile radio telephone systems, the quality of the signal received by a mobile station from a base station is affected from time to time by natural phenomena inherent in the use of radio signals to communicates. A factor common to most of the problems related to radio reception is that a desired signal at the receiver is too weak, either in comparison to thermal noise or in comparison to an interfering signal. The interfering signal can be characterized as any undesired signal received on the same channel by the receiver as the desired signal. Another common transmission problem in mobile radio systems used in an environment where there are objects such as buildings present, is that of log-normal fading. This phenomenon occurs as a result of a shadowing effect produced by buildings and natural obstacles such as hills located between the transmitting and receiving antennas of the mobile station and the base station. As the mobile station moves around within the environment, the received signal strength increases and decreases as a function of the type of obstacles which are at that moment between the transmitting and receiving antennas.
Another phenomenon which effects signal strength within the mobile radio system operated in an urban environment is that of Rayleigh fading. This type of signal degradation occurs when a broadcast signal takes more than one path from the transmitting antenna to the receiving antenna so that the receiving antenna of the mobile station receives not just one signal but several. One of these multiple signals may come directly from the transmitting antenna but several others are first reflected from buildings and other obstructions before reaching the receiving antenna and, thus, are delayed slightly in phase from one another. The reception of several versions of the same signal shifted in phase from one another results in a vector sum of the signals being the resultant composite signal actually received at the receiving antenna. In some cases, the vector sum of the received signal may be very low, even close to zero, resulting in a fading dip wherein the received signal virtually disappears. In the cases of a moving mobile station, the time that elapses between two successive fading dips due to Rayleigh fading depends on both a frequency of the received signal and a speed at which the mobile station is moving.
In case of digitally modulated radio systems, such as those in which time division multiple access (TDMA) is used, other radio transmission difficulties arise. One of these difficulties, referred to as time dispersion, occurs when a signal representing certain digital information is interfered with at the receiving antenna by different, consecutively transmitted symbols due to reflections of the original signal from an object far away from the receiving antenna. It thus becomes difficult for the receiver to decide which actual symbol is being detected at the present moment. Another transmission phenomenon inherent in the use of digitally modulated signals, such as TDMA, is due to the fact that each mobile station must only transmit during a particular allocated time slot of the TDMA frame and remain silent during the other times. Otherwise, the mobile will interfere with calls from other mobile stations which are assigned different time slots of the same frame.
One technique used to deal with the time dispersion of digitally modulated signals and the resultant inter-symbol interference is equalization within the receiver. Since an optimum receiver is adapted to the particular type of channel used for the transmission, equalization creates a mathematical model of the channel and adjusts the receiver to this model. If the receiver knows how long and how strong the signal reflections are, it can take this into account when the received signal burst is detected. In the mobile radio environment, an equalizer creates a model of the transmission channel, eg. the air interface, and calculates the most probable transmitted sequence of data within that channel. For example, TDMA digitally modulated data is transmitted in bursts which are placed within discrete time slots. A "training sequence" comprising data of a known pattern and with good auto-correlation properties is placed somewhere in each burst. This training sequence is used by the equalizer to create the channel model. The channel model may change with time, so that it may be tracked during each burst.
The training procedure within the equalizer may also involve correlating the received signal burst with one or more shifts of the training pattern to determine a corresponding number of points (both phase and amplitude) of the channel's impulse response.
An MLSE equalizer typically implements a linear, finite-impulse-response (FIR) model of the channel, that is, a transversal filter or a tapped delay line having complex multiplication weights applied to the tap outputs. The weighted outputs are summed to predict, for each possible data symbol pattern that can be contained within the time span of the channel's impulse response, the signal waveform that should be received for the next data symbol. The predicted waveforms are compared with the actually received waveform and metrics for and against the probability of each data symbol pattern being "correct" (i.e., the pattern received) are accumulated. Each metric is based on the accuracy of the match between the predicted waveform and the received waveform. The data symbol patterns that can be contained within the time span of the channel's impulse response correspond to the "states" of the system. Such equalizers are sometimes referred to as "Viterbi" equalizers and are described in J. G. Proakis, Digital Communication, 2d ed, New York,: McGraw-Hill, Sections 6.3 and 6.7 (1989).
The weights applied to the delay line tap outputs are the J coefficients, C.sub.1, C.sub.2, C.sub.3 . . . C.sub.j in the equation: EQU S.sub.i =c.sub.1 D.sub.i +c.sub.2 D.sub.i-1 +c.sub.3 D.sub.i-2 . . . C.sub.j D.sub.i-j+1 ;
where S.sub.i is the predicted signal for the sequence of data symbol patterns D.sub.i, D.sub.i-1, D.sub.i-2 . . . The coefficients are usually calculated from the known training pattern. In the case of signaling by binary data symbols, (i.e., 1 and 0), the number of predicted signals that must be calculated is 2.sup.j. It is understood that M'ary (e.g. quaternary) data symbols can also be used.
Various methods for optimally updating the channel model from the received signal are known, such as that described in European patent application no. 90850301.4, filed Sep. 10, 1990. The best methods maintain a separate channel model for each Viterbi state and, when one of the states is selected as the best predecessor of a new state, the channel model corresponding to that state is updated and becomes the channel model for the new state. Thus, it is ensured that the channel models are always derived from the best demodulated data sequences received up to that time.
U.S. Pat. No. 5,331,666 to Dent entitled "Adapted Maximum Likelihood Modulator" describes a variation of the adaptive Viterbi equalizer that does not employ channel models to generate the predictions except during system start-up, and thus does not update the channel model parameters Rather, direct updating of the signals predictions for each state, without going through the intermediate step of first updating the channel models is described in the Dent '666 patent which is hereby incorporated by reference herein.
Viterbi equalizers incorporate the following steps in performing their functions: (1) determining the tap coefficients of a Finite Impulse Response (FIR) model of the channel; (2) for all possible data symbol sequences that can be postulated to be contained within the impulse response length of the channel model, predicting the signal value that should be received based upon the determined tap coefficients; (3) comparing each postulated value with the actually received signal value and calculating the mismatch (usually by squaring the difference between the received and postulated values); (4) for each postulated symbol sequence, adding the calculated mismatch to the cumulative mismatch of predecessor sequences that are consistent with the postulated symbol sequence, also called "the state" (the cumulative mismatch values are called "path metrics"); and (5) choosing the "best" of the possible predecessor sequences that can transition to the new postulated state, i.e., choosing the predecessor sequence that gives the lowest path metric for the new state. Thus, the path metrics can be considered confidence factors that represent the degrees of correlation between the postulated symbol sequences and the actually received signal.
It should be appreciated that the Viterbi equalizer is a form of sequential maximum likelihood sequence estimator (MLSE) that decodes, or demodulates, the received data symbol stream. MLSE estimators and other equalization methods are described in the reference by J. G. Proakis, above.
FIG. 1 illustrates the data structure and flow within an MLSE equalizer having 16 states, the predicted signal values being assumed to depend on four previous binary symbols (bits) plus one new bit. The channel impulse response length (J) for this example is thus five symbols, i.e., the latest echo can be four symbols delayed compared to the shortest signal path.
Referring to FIG. 1, an MLSE processing cycle begins by assuming the postulated symbol history of state 0000 to be true, and that a new bit "0" was transmitted. Consequently, causing the channel model 40, the signal value that should be observed given the 5-bit symbol history 00000 predicted. This is compared in comparator 50 with the actual received signal value and a mismatched value produced. This is added in adder 51 with the previous state 0000 path metric to produce a candidate metric for a new 0000 state.
However, another candidate for the new path metric of new state 0000 can be derived by assuming state 1000 to contain the true history, with a new bit of `0`. This is because both 0000-0 and 1000-0 lead to a new state (0-0000) when the oldest (left-most) bit is left shifted out of the 4-bit state number and into the symbol history memory. This candidate is evaluated by applying 10000 to the channel model 40, comparing the prediction so-obtained with the input signal value in comparator 50 and adding the resultant mismatch with the previous cumulative mismatch (path metric) associated with state 1000 in adder 52. The two candidate values from adders 51 and 52 are then compared in a comparator 53, and the lower of the two is selected to become the new path metric of new state 0000. Furthermore, the contents of the history memory 55 associated with the selected predecessor state is selected to be the symbol history of the new state 0000. Also, the selected bit history is left-shifted and a 0 or 1 entered in the right-most position according as state 0000 or 1000 gave rise to the selected candidate path metric.
The above procedure is then repeated with the assumption that the new bit is a `1` in order to produce a new state 0001, also with either state 0000 or 1000 as candidate predecessors
The above procedure is then repeated using every pair of states, which are 8 states apart, to derive all 16 new states, as follows:
0001,1001 to produce new states 0010 and 0011 PA1 0010,1010 to produce new states 0100 and 0101 PA1 0011,1011 to produce new states 0110 and 0111 PA1 0100,1100 to produce new states 1000 and 1001 PA1 0101,1101 to produce new states 1010 and 1011 PA1 0110,1110 to produce new states 1100 and 1101 PA1 0111,1111 to produce new states 1110 and 1111
At the end of the above processing cycle, one signal sample has been processed and one extra bit has been demodulated and inserted into symbol history memories 55. There is a tendency for the older bits in the history memories to converge to the same value, at which point that bit can be extracted as a final unambiguous decision and the history memory shortened 1 bit. Other methods of truncating history memory growth are known to the art, such as taking the oldest bit from the state having the lowest path metric. If memory is sufficient, bits need not be extracted until all received values have been processed.
It will be understood that the MLSE equalizer recognizes that some sequences of data symbol patterns, and thus some sequences of predicted waveforms, are not valid. For example, a prediction that the channel carried the binary data symbol pattern 10010 at one instant (i.e, one bit period) and a prediction that the channel carried the binary data symbol pattern 11001 at the next instant (i.e., the next bit period) are inconsistent because the pattern 10010 can be followed only by the patterns 00100 or 00101 (assuming a left-shift in passing through the channel.) Also under such conditions, each of the 00100 and 00101 patterns can have only either 10010 or 00010 as predecessors. Thus, a set of transition rules constrains the number of ways the metrics can be sequentially accumulated for each sequence of predicted waveforms.
It will be appreciated that such prior demodulators operate on the received signal only in the forward direction: a received training pattern is used to develop predicted waveforms for yet-to-be-received data symbols. If the training pattern is lost or excessively distorted due to severe channel fading, intersymbol interference, frequency errors, etc., such forward demodulators must wait until the next training pattern is successfully received before they are able to demodulate accurately. As a result, data sent in the intervening periods between training patterns can be lost.
In U.S. Pat. No. 5,335,250 to Dent et al. entitled "Method and Apparatus for Bidirectional Demodulation of Digitally Modulated Signals" a technique is disclosed and claimed for minimizing the loss of data sent during intervening periods between training patterns. This technique includes the demodulation of intervening data not only forward from a received training pattern but also backwards from the next received training pattern. In general, this technique is implemented by storing a sequence of received signal samples, time-reversing the stored sequence, and estimating quality factors for both forward and backward demodulation of the stored and time reversed sequence, respectively, to determine how many data symbols should be decoded by forward demodulation and how many should be decoded by backward demodulation.
In the technique set forth in the '250 patent, the criterion decide which direction to continue demodulation from the training pattern is based upon the metric in the MLSE equalizers which is typically related to the noise level within the received data. Since the demodulated she demodulated signal depends not only upon the level of noise but also the signal strength, a technique for determining which direction to demodulate from the training pattern which considers other parameters related to both signal strength and noise results in a better performance. The system of the present invention incorporates such a technique.